Nanotheranostics: Platforms, Current Applications, and Mechanisms of Targeting in Breast and Prostate Cancers
Bibliographic record
Abstract
Globally, cancer is one of the deadliest diseases, needing a meticulous diagnosis and targeted treatment plan to achieve an initial prognosis, followed by precision and optimization in treatment. Nonselective targeting, difficulty in accurately monitoring treatment end-results, serious drug side-effects, and severity of disease resulting in metastasis are the key flaws of traditional techniques. Nanotechnology and nanoparticles possess special features to completely transform the field of diagnosis and treatment of cancer. A holistic strategy that employs a dual function of diagnosis and therapy while utilizing a nanocarrier is referred to as a nanotheranostic. The nanotheranostic framework was created to surmount a variety of biological and physiological obstacles, effectively delivering the cargo to the intended target location, while simultaneously facilitating therapeutic intervention, surveillance, and validation to demonstrate improved treatment effectiveness. As a result, a nanotheranostic platform can be useful for targeted drug delivery, release, and distribution assessment, in addition to patient classification and survival. Nanotheranostic techniques also lead to reduced drug side-effects compared with conventional therapies. In this review, we outline current studies on nanotheranostics and their advantages over conventional treatment strategies, the applications and challenges/limitations of nanotheranostics, and the mechanisms of targeting in breast and prostate cancers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".